Methods used by WHO to estimate the
global burden of TB disease
Glaziou P, Sismanidis C, Zignol M, Floyd K
Global TB Programme, World Health Organization, Geneva, Switzerland
Abstract
This paper describes methodological details used by WHO in 2016 to estimate TB
incidence, prevalence and mortality. Incidence and mortality are disaggregated by HIV
status, age and sex. Methods to derive MDR-TB burden indicators are detailed. Four
main methods were used to derive incidence: (i ) case notification data combined with
expert opinion about case detection gaps (74 countries representing 22% of global
incidence in 2015); (ii ) results from TB prevalence surveys (20 countries, 62% of global
incidence); (iii ) notifications in high-income countries adjusted by a standard factor to
account for under-reporting and underdiagnosis (118 countries, 15.5% of global
incidence) and (iv ) capture-recapture modelling (5 countries, 0.5% of global incidence).
Mortality was obtained from national vital registration systems of mortality surveys in
128 countries (52% of global HIV-negative TB mortality). In other countries, mortality
was derived indirectly from incidence and case fatality ratio.
Introduction
Estimates of the burden of disease caused by TB and measured in terms of incidence,
prevalence and mortality are produced annually by WHO using information gathered
through surveillance systems (case notifications and death registrations), special studies
(including surveys of the prevalence of disease), mortality surveys, surveys of
under-reporting of detected TB, in-depth analysis of surveillance and other data, expert
opinion and consultation with countries. In June 2006, the WHO Task Force on TB
Impact Measurement was established1, with the aim of ensuring that WHO’s assessment
of whether 2015 targets were achieved should be as rigorous, robust and
consensus-based as possible. The Task Force reviewed methods and provided
recommendations in 2008, 2009 and most recently in March 2015 and April 2016.
Historical background
Historically, a major source of data to derive incidence estimates were results from
tuberculin surveys conducted in children2. Early studies showed the following
relationship between the annual risk of infection denoted λ and the incidence of smear
positive TB denoted I s+: one smear positive case infects on average 10 individuals per
year for a period of 2 years and a risk of infection of 10-2y-1 corresponds approximately to
an incidence rate of 50 10-5y-1. However, this relationship no longer holds in the ×
context of modern TB control and in HIV settings3. In addition to uncertainty about the
relationship between λ and I s+, estimates of incidence obtained from tuberculin surveys
suffer from other sources of uncertainty and bias, including unpredictable diagnostic
performance of the tuberculin test4, digit preference when reading and recording the
size of tuberculin reactions5, sensitivity to assumptions about reaction sizes attributed to
infection6, sensitivity to the common assumption that the annual risk of infection is age
invariant, and lastly, sensitivity of overall TB incidence estimates to the assumed
proportion of TB incidence that is smear positive.
A first global and systematic estimation exercise led by WHO in the early 1990s
estimated that there were approximately 8 million incident TB cases in 1990 (152 ×
10-5y -1) and 2.6-2.9 million deaths (46-55 10-5y-1)7. A second major reassessment was ×
published in 19998, with an estimated 8 million incident cases for the year 1997 (136 ×
10-5y -1), and 1.9 million TB deaths (32 10-5y -1). The most important sources of ×
information were case notification data for which gaps in detection and reporting were
obtained from expert opinion. In addition, data from 24 tuberculin surveys were
translated into incidence and 14 prevalence surveys of TB disease were used.
Incidence
TB incidence has never been measured through population based surveys at national
level because this would require long-term studies among large cohorts of people
(hundreds of thousands), involving high costs and challenging logistics. Notifications of
TB cases provide a good proxy indication of TB incidence in countries that have both
high-performance surveillance systems (for example, there is little under-reporting of
diagnosed cases) and where the quality of and access to health care means that few cases
remain undiagnosed. In the large number of countries where these criteria are not yet
met, better estimates of TB incidence can be obtained from an inventory study. An
inventory study is a survey to quantify the level of under-reporting of detected TB cases;
if certain conditions are met, capture-recapture methods can also be used to estimate TB
incidence 9.
The ultimate goal of TB surveillance is to directly measure TB incidence from national
case notifications in all countries. This requires a combination of strengthened
surveillance, better quantification of under-reporting (i.e. the number of newly
diagnosed cases that are missed by surveillance systems) and universal access to health
care (to minimize under-diagnosis of cases). A TB surveillance checklist developed by
the WHO Global Task Force on TB Impact Measurement defines the standards that
need to be met for notification data to provide a direct measure of TB incidence10.
Methods currently used by WHO to estimate TB incidence can be grouped into four
major categories. Figure 1 shows the distribution of countries according to the four
categories:
1. Case notification data combined with expert opinion about case
detection gaps. Expert opinion, elicited in regional workshops or country
missions, is used to estimate levels of under-reporting and under-diagnosis. Trends
are estimated using either mortality data, surveys of the annual risk of infection or
exponential interpolation using estimates of case detection gaps for three years. In
this report, this method is used for 74 countries that accounted for 22% of the
estimated global number of incident cases in 2015.
2. Results from TB prevalence surveys. Incidence is estimated using prevalence
survey results and derived from a model that accounts for the impact of HIV on the
distribution of disease duration. This method is used for 20 countries (19 with
national survey data and one – India – with a survey in one state) that accounted for
62% of the estimated global number of incident cases in 2015.
3. Notifications in high-income countries adjusted by a standard factor to
account for under-reporting and under-diagnosis. This method is used for
118 countries: all high-income countries except the Netherlands and the United
Kingdom, plus selected upper-middle income countries with low levels of
under-reporting, including Brazil and China. For three countries (France, Republic
of Korea, Turkey) the adjustment was country-specific, based on results from studies
of under-reporting. These 118 countries accounted for 15.5% of the estimated global
number of incident cases in 2015.
4. Results from inventory/capture-recapture studies. This method is used
for 5 countries: Egypt, Iraq, the Netherlands, the United Kingdom and Yemen. They
accounted for 0.5% of the estimated global number of incident cases in 2015.
Four main methods
Method 1 - Case notification data combined with expert opinion about case detection
gaps.
Expert opinion, elicited in regional workshops, national consensus workshops or
country missions, is used to estimate levels of under-reporting and under-diagnosis.
Trends are estimated using either mortality data, national repeat surveys of the annual
risk of infection or exponential interpolation using estimates of case detection gaps for
three years. The estimation of case detection gaps is essentially based on an in-depth
analysis of surveillance data; experts provide their educated best guess about the range
of the plausible detection gap g
where I denotes incidence, N denotes case notifications, f denotes a cubic spline
function in countries with large year-to-year fluctuations in N , or else, the identity
function. The incidence series are completed using assumptions about changes in CFR
over time in countries with evidence of improvements in TB prevention and care, such
as increased detection coverage over time or improved treatment outcomes, ensuring
that the following inequality holds
where M denotes mortality.
A full description of the methods used in regional workshops where expert opinion was
systematically elicited following an in-depth analysis of surveillance data is publicly
available in a report of the workshop held for countries in the African Region (in Harare,
Zimbabwe, December 2010)11. In some countries, case reporting coverage changed
significantly during the period 2000-2015 as a result of disease surveillance reforms
(e.g. disease surveillance was thoroughly reformed after the SARS epidemic in China,
the Ministry of Justice sector notified cases among prisoners in Russia starting in the
early 2000s). Trends in incidence were derived from repeat tuberculin survey results in
Bhutan, India and Yemen and from trends in mortality in 40 countries (including most
countries in Eastern Europe).
The proportion of cases that were not reported were assumed to follow a Beta
distribution, with parameters α and β obtained from the expected value E and variance
V using the method of moments12, as follows
(1)
Time series for the period 2000–2014 were built according to the characteristics of the
levels of under-reporting and under-diagnosis that were estimated for the three
reference years. A cubic spline extrapolation of V and E , with knots set at the reference
years, was used for countries with low-level or concentrated HIV epidemics. In countries
with a generalized HIV epidemic, the trajectory of incidence was based on the annual
rate of change in HIV prevalence and time changes in the fraction F of incidence
attributed to HIV, determined as follows
where h is the prevalence of HIV in the general population, ρ is the TB incidence rate
ratio among HIV-positive individuals over HIV-negative individuals and ϑ is the
prevalence of HIV among new TB cases.
If there were insufficient data to determine the factors leading to time-changes in case
notifications, incidence was assumed to follow a horizontal trend going through the
most recent estimate of incidence.
Limitations of the method based on eliciting expert opinion about gaps in case detection
and reporting included a generally small number of interviewed experts; lack of clarity
about vested interests when eliciting expert opinion; lack of recognition of
over-reporting (due to over-diagnosis, e.g. in some countries of the former Soviet Union
implementing a large-scale systematic population screening policy that may result in
many people with abnormal chest X-ray but no bacteriological confirmation of TB
disease being notified and treated as new TB cases); incomplete data on laboratory
quality and high proportion of patients with no bacteriological confirmation of diagnosis
are a potential source of error in estimates.
Method 2 - Results from TB prevalence surveys.
Two approaches were used to derive incidence from prevalence.
In a first approach, incidence is estimated using measurements from national surveys of
the prevalence of TB disease combined with estimates of the duration of disease.
Incidence is estimated as the prevalence of TB divided by the average duration of
disease assuming epidemic equilibrium: let N denote the size of a closed population with
the number of birth and deaths the same for a period Δt >0, let C be the number of
prevalent TB cases, P the prevalence rate so that P =C /N . Let m denote the rate of exit
from the pool of prevalent cases through mortality, spontaneous self-cure or cure from
treatment, and I the rate new cases are added to the pool. At equilibrium during the
time period Δt and further assuming exponentially distributed durations d such that
d =m -1
(3)
In practice, the average duration of presence in the pool of prevalent cases cannot be
directly measured. For example, measurements of the duration of symptoms in
prevalent TB cases that are detected during a prevalence survey are systematically
biased towards lower values, since survey investigations truncate the natural history of
undiagnosed disease. Measurements of the duration of disease in notified cases ignore
the duration of disease among non-notified cases and are affected by recall biases.
Literature reviews have provided estimates of duration of disease in untreated TB cases
from the pre-chemotherapy era (before the 1950s). The best estimate of the mean
duration of untreated disease (for smear-positive cases and smear-negative cases
combined) in HIV-negative individuals is about three years. There are few data on the
duration of disease in HIV-positive individuals. The assumed distributions of disease
durations are shown in Table 1.
A second approach consists of estimating disease duration using three model
compartments: susceptibles (S ), untreated TB (U ) and treated TB (T ). The size of U and
T is obtained from the prevalence survey. Transitions from U to T are determined as
follows
Where I denotes Incidence, μ and θ denote mortality and self-cure or cure (with
subscripts u and t indicating untreated and treated cases), respectively, δ denotes the
rate of removal from U through detection and treatment. At equilibrium, the above two
equations simplify to
Disease duration (untreated) is obtained from
where
is the proportion of incidence that dies or self-cures before treatment. π is assumed
distributed uniform with bounds 0 and 0.1. Table 2 shows estimates of incidence from
four recent prevalence surveys using this method.
Among limitations of this method is the insufficient power of surveys to estimate the
number of prevalent TB cases on treatment with great precision. Further, in most
surveys, cases found on treatment during the survey do not have a bacteriological status
at onset of treatment documented based on the same criteria as survey cases
(particularly when culture is not performed routinely). The method, however, provides
more robust estimates of incidence compared with those obtained from expert opinion
(method 1). Figure 2 shows recent changes in estimates as new data became available.
In countries with high-level HIV epidemics that completed a prevalence survey, the
prevalence of HIV among prevalent TB cases was found systematically lower than the
prevalence of HIV among newly notified TB cases, with an HIV prevalence rate ratio
among prevalent TB over notified cases ranging from 0.07 in Rwanda (2012) to 0.5 in
Malawi (2013). The HIV rate ratio was predicted from a random-effects model fitting
data from 6 countries with data collected over the period 2012-2015 (Malawi, Rwanda,
Tanzania, Uganda, Zambia and Zimbabwe), using a restricted maximum likelihood
estimator and setting HIV among notified cases as an effect modifier13, using the R
package metafor14 (Figure 3). The model was then used to predict HIV prevalence in
prevalent cases from HIV prevalence in notified cases in African countries that were not
able to measure the prevalence of HIV among survey cases.
The above two methods to derive incidence from prevalence are compared in Table 3. It
is not clear which method will perform better. The second method requires a sufficient
number of cases on treatment at the time of the survey (as a rule of thumb, at least 30
cases) to generate stable estimates. When both methods can be applied (so far only in
low-HIV settings), results from two methods may be combined in a statistical ensemble
approach as follows:
The incidence rate obtained using method i is assumed distributed Beta with shape and
scale parameters α i +1 and β i +1, respectively, and determined using the method of
moments based on equation 3: I i∼ B (α i +1,β i +1) so that
The combined probability is then expressed as
(4)
Method 3 - Notifications in high-income countries adjusted by a standard factor to
account for under-reporting and under-diagnosis.
TB surveillance systems from countries in the high-income group and selected countries
in the upper-middle income group were assumed to perform similarly well on average.
The exceptions were the Republic of Korea, where the under-reporting of TB cases has
recently been measured using annual inventory studies and France, where the estimated
level of under-reporting was communicated by public health authorities, based on
unpublished survey results. In the United Kingdom and the Netherlands, incidence was
obtained using capture-recapture modeling (see next section). Surveillance data in this
group of countries are usually internally consistent. Consistency checks include
detection of rapid fluctuations in the ratio of TB deaths / TB notifications (M /N ratio),
which may be indicative of reporting problems.
Method 4 - Capture-recapture modelling.
This method was used for 5 countries: Egypt15, Iraq16, the Netherlands17, the United
Kingdom18 and Yemen19. Capture-recapture modelling was considered in studies with at
least 3 lists and estimation of list dependencies9. The estimate of the surveillance gap in
the UK and the Netherlands was assumed time invariant. In Yemen, trends in incidence
were derived from results of two consecutive tuberculin surveys20. In Egypt and Iraq,
trends were derived using methods described in section describing method 1.
HIV-positive TB incidence
Provider-initiated testing and counselling with at least 50% HIV testing coverage is the
most widely available source of information on the prevalence of HIV in TB patients.
However, this source of data is affected by selection biases, particularly when coverage is
closer to 50% than to 100%. As coverage of HIV testing continues to increase globally,
biases will decrease. Other sources of information on the prevalence of HIV among new
TB cases include sero-surveys of a random sample of newly diagnosed TB cases and HIV
sentinel surveillance systems when they include TB as a sentinel group. The different
data sources were combined using local polynomial regression fitting by weighted least
squares, using weight values of 5 for data from a nationally representative survey, 0.8
for data based on HIV sentinel surveillance, and a value equal to testing coverage in the
case of data from provider-initiated HIV testing with coverage greater than 50%, and
zero weights when testing coverage was less than 50%. In countries with no surveillance
data on HIV among TB cases, the prevalence of HIV was derived indirectly from the
prevalence of HIV in the general population, based on the relationship between the
prevalence of HIV in TB and the prevalence of HIV in the general population shown in
Annex 2. The TB incidence rate ratio (HIV-positive/HIV-negative) was 19 (17 – 22) in
2015.
Disaggregation by age and sex
Estimates for men (males aged ≥15 years), women (females aged ≥15 years) and
children (aged <15 years) are derived as follows. Age and sex disaggregation of
smear-positive tuberculosis case notifications has been requested from countries since
the establishment of the data collection system in 1995, but with few countries actually
reporting these data to WHO. In 2006, the data collection system was revised to
additionally monitor age disaggregated notifications for smear-negative and
extrapulmonary tuberculosis. The revision also included a further disaggregation of the
0–14 age group category to differentiate the very young (0–4) from the older children
(5–14). While reporting of age disaggregated data was limited in the early years of the
data collection system, reporting coverage kept improving. For 2012 case notifications,
age-specific data reached 99%, 83% and 83% of total smear-positive, smear-negative
and extrapulmonary tuberculosis global case notifications. Finally in 2013, another
revision of the recording and reporting system was necessary to allow for the capture of
cases diagnosed using WHO-approved rapid diagnostic tests (such as Xpert
MTB/RIF)21. This current revision requests the reporting of all new and relapse case
notifications by age and sex. Global progress in reporting of TB cases among children is
shown in Figure 4.
While there are some nationwide surveys that have quantified the amount of
under-reporting of cases diagnosed in the health sector outside the network of the
NTPs15,17,22, none have produced precise results by age. Small-scale, convenient-sampled
studies indicate that under-reporting of childhood tuberculosis can be very high23,24 but
extrapolation to national and global levels is not yet possible. Plans for implementation
of nationwide surveys are under way in selected countries to measure under-reporting
of tuberculosis in children25.
Results from two methods are combined to estimate TB incidence in children, using a
statistical ensemble approach based on equation 4. The first method estimates the
proportion of all TB cases that are in children as a function of expected age-specific
proportions of smear positive TB, according to a previously published approach26
updated to incorporate recent data27. The second method is based on a dynamic model
that simulates the course of natural history of TB in children, starting from estimates of
tuberculous infection in children as a function of demographic and adult TB prevalence
and subsequently modelling progression to pulmonary and extra-pulmonary
tuberculosis disease taking into account country-level BCG vaccination coverage and
HIV prevalence28.
Using the sex disaggregated reporting of TB case notification data we calculated the
ratio of the number of TB cases notified in men compared with women as a measure of
the ratio r 1 for incident cases, assuming no sex differential in the detection of incident
cases. Evidence from prevalence surveys consistently show bigger recording and
detection gaps in men29. The assumption of no sex differential in the detection of
incident cases may thus lead to underestimating the proportion of men among incident
cases. With currently available data, it is not possible to estimate male and female case
detection ratios for all countries.
Overall incidence in adults 15 years or over (I a ) can be disaggregated into estimates
among men (I m ) and women (I w ) as shown
where r 1 = I m /I w and I a =I m +I w .
(6)
Producing estimates of TB incidence among children is challenging primarily due to the
lack of well performing diagnostics to confirm childhood TB and the lack of age-specific,
nationwide, robust survey and surveillance data.
Drug resistance
Following a revision of policy recommendations for the treatment of drug-resistant TB
issued by WHO in May 201630 all people with TB resistant to rifampicin with or without
resistance to other drugs should be treated with an MDR-TB regimen. This includes
patients with MDR-TB as well as any other patient with TB resistant to rifampicin. For
this reason all global, regional, and country level estimates of incidence and mortality of
drug-resistant TB published in this report refer to cases of TB resistant to rifampicin,
with or without additional resistance to other drugs (MDR/RR-TB).
Global and regional estimates of the proportion of new and retreatment cases of TB that
had MDR/RR-TB in 2015 were calculated using country-level information. If countries
had reported data on the proportion of new and retreatment cases of TB that have
rifampicin resistance from routine surveillance or a survey of drug resistance, the latest
available information was used. For data from routine surveillance to be considered
representative, at least 60% of notified new pulmonary TB cases must have a
documented DST result for at least rifampicin. For retreatment cases, some surveys are
also considered if at least 75% retreatment cases have a documented DST result for at
least rifampicin. For countries that have not reported such data, estimates of the
proportion of new and retreatment cases of TB that have MDR/RR-TB were produced
using modelling (including multiple imputation) that was based on data from countries
for which data do exist. Estimates for countries without data were based on countries
that were considered to be similar in terms of TB epidemiology. The observed and
imputed estimates of the proportion of new and retreatment cases of TB that have
MDR/RR-TB were then pooled to give a global estimate, with countries weighted
according to their share of global notifications of new and retreatment cases.
MDR/RR-TB incidence
First we calculate proportions of new (p n ) and retreated (p r ) patients with MDR/RR-TB
resistance from routine surveillance or survey of drug resistance data. For each of the
new and retreated TB patient groups, these proportions are calculated as the sum of
patients who are:
(i) MDR-TB among those with DST results for Rifampicin (R) and Isoniazid (H)
(ii) R but not H resistant TB among those with DST for R and H, and
(iii) RR-TB among those with a gene Xpert result for R
divided by the sum of patients
(iv) with DST results for R and H, and
(v) gene Xpert result for R.
We then estimate MDR/RR-TB incidence (I mdr/rr ) by adding the expected number of
RR-TB cases among three distinct types of TB case notifications: (i) new all forms (new )
multiplied by the proportion MDR/RR-TB among new (p n ) from DRS, inflated upwards
to adjust for the estimated detection gap of incident cases not identified by the
surveillance system (cdr ), (ii) relapse all forms (rel ) multiplied by the proportion
MDR/RR-TB among relapses (p r ) approximated by p n RR (risk ratio of being a DR-TB ×
patient when a relapse compared to a new TB patient) inflated by a lower detection gap
(cdr u ), and (iii) all retreatments that are not relapse forms (ret notrel ) multiplied by the
proportion MDR/RR-TB among retreatment cases from DRS (p r ) inflated by the lower
detection gap (cdr u ), using a uniform distribution bounded by cdr and 1. Patients with a
previous TB episode are more likely to self-present or be screened for TB, hence the
assumption of a lower detection gap (an approach recommended by the WHO Global
Task Force on TB Impact Measurement).
MDR/RR TB mortality
The VR mortality data reported to WHO by Member States does not differentiate
between MDR-TB and non-MDR-TB as a cause of death (there is no specific ICD-9 or
ICD-10 codes for MDR-TB, although countries such as South Africa have allocated two
specific codes U51 and U52 to classify deaths from MDR-TB and XDR-TB
respectively)31. Therefore, a systematic review and meta-analysis of the published
literature was undertaken to estimate the relative risk of dying from MDR-TB compared
with non MDR-TB. We are assuming this relative risk of death is the same as that for
MDR/RR-TB. The global estimate of MDR/RR-TB deaths is based on the following
formula:
Where:
m = global MDR/RR-TB mortality,
M = global TB mortality,
p = overall proportion of MDR/RR-TB among prevalent TB cases, approximated by the
weighted average of the proportion of new and retreated cases that have MDR/RR-TB,
r = the relative risk of dying from MDR/RR-TB versus non-MDR/RR-TB.
Prevalence from population-based surveys
The best way to measure the prevalence of TB is through national population-based
surveys of TB disease32,33. Measurements of prevalence are typically confined to the
adult population, exclude extrapulmonary cases and do not allow the diagnosis of cases
of culture-negative pulmonary TB.
TB prevalence all forms and all ages (P ) is measured as: bacteriologically-confirmed
pulmonary TB prevalence (P p ) among those aged ≥15 measured from national survey
(P a ), adjusted for pulmonary TB in children (P c ) and the proportion e of
extra-pulmonary TB all ages
where c is the proportion of children among the total country population.
The estimate of overall prevalence P is affected by sampling uncertainty (relative
precision is typically about 20%), and uncertainty about e (of note, values for e vary
widely among countries with high-performance TB surveillance) and P c . The quality of
routine surveillance data to inform levels of pulmonary TB in children and
extra-pulmonary TB for all ages is often questionable.
Mortality
The best sources of data about deaths from TB (excluding TB deaths among
HIV-positive people) are vital registration (VR) systems in which causes of death are
coded according to ICD-10 (although the older ICD-9 and ICD-8 classification are still in
use in several countries), using ICD-10: A15-A19 and B90 codes, equivalent to ICD-9:
010-018, and 137. When people with AIDS die from TB, HIV is registered as the
underlying cause of death and TB is recorded as a contributory cause. Since one third of
countries with VR systems report to WHO only the underlying causes of death and not
contributory causes, VR data usually cannot be used to estimate the number of TB
deaths in HIV-positive people. Two methods were used to estimate TB mortality among
HIV-negative people:
● direct measurements of mortality from VR systems or mortality surveys (128
countries, in green in Figure 5);
● indirect estimates derived from multiplying estimates of TB incidence by
estimates of the CFR.
Estimating TB mortality among HIV-negative people from vital
registration data and mortality surveys
As of July 2015, mortality data from 128 countries were used, representing 52% of the
estimated number of TB deaths (among HIV-negative TB) globally in 2015. The VR data
on TB deaths from Zimbabwe were not used because large numbers of HIV deaths were
miscoded as TB deaths. Improved empirical adjustment procedures for such miscoding
have recently been published34. Estimates for India and for South Africa (adjusted for
HIV/TB miscoding) were obtained from the Institute of Health Metrics and Evaluation
at http://vizhub.healthdata.org/cod/, readjusted to fit WHO mortality envelopes (the
estimated number of deaths in total) by using a multiplication factor equal to the ratio of
WHO to IHME envelopes. Results from mortality surveys were used to estimate TB
mortality in India and Viet Nam as an interim measure.
Among the countries for which VR data could be used (Figure 5), there were 1518
country-year data points 2000–2015, after 10 outlier data points from systems with very
low coverage (<20%) as estimated by WHO35 or very high proportion of ill-defined
causes (>50%) were excluded for analytical purposes.
Reports of TB mortality were adjusted upwards to account for incomplete coverage
(estimated deaths with no cause documented) and ill-defined causes of death (ICD-9:
B46, ICD-10: R00–R99)35. It was assumed that the proportion of TB deaths among
deaths not recorded by the VR system was the same as the proportion of TB deaths in
VR-recorded deaths. For VR-recorded deaths with ill-defined causes, it was assumed
that the proportion of deaths attributable to TB was the same as the observed
proportion in recorded deaths. The adjusted number of TB deaths κ a was obtained from
the VR report κ as follows:
where v denotes coverage (i.e. the number of deaths with a documented cause divided
by the total number of estimated deaths) and g denotes the proportion of ill-defined
causes. The uncertainty related to the adjustment was estimated as follows:
The uncertainty calculation does not account for miscoding, such as HIV deaths
miscoded as deaths due to TB, except in South Africa.
Missing data between existing adjusted data points were interpolated. Trailing missing
values were predicted using exponential smoothing models for time series36. A penalized
likelihood method based on the in-sample fit was used for country-specific model
selection. Leading missing values were similarly predicted backwards to 2000.
In 2015, 52% of global TB mortality (excluding HIV) was directly measured from VR or
survey data (or imputed from survey or VR data from previous years). The remaining
was estimated using the indirect methods described in the next section.
Estimating TB mortality among HIV-negative people from
estimates of case fatality rates and TB incidence
In countries lacking mortality data of the necessary coverage and quality, TB mortality
was estimated as the product of TB incidence and the case fatality rate (CFR) after
disaggregation by case type as shown in Table 4, following a literature review of CFRs by
the TB Modelling and Analysis Consortium (TB-MAC):
(5)
where M denotes mortality, I incidence. f u and f t denote CFRs untreated and treated,
respectively and the superscript denotes HIV status. T denotes the number of treated TB
cases. In countries where the number of treated patients that are not notified
(under-reporting) is known from an inventory study, the number of notified cases is
adjusted upwards to estimate T - accounting for under-reporting.
Figure 6 shows a comparison of 126 direct mortality estimates for 2015 and indirect
estimates obtained from the CFR approach for the same countries. Of note, countries
with VR data tend to be of a higher socio-economic status compared with countries with
no VR data where the indirect approach was used.
Estimating TB mortality among HIV-positive people
TB mortality among HIV-positive is calculated using equation 5, exchanging
superscripts - with +. The case fatality ratios were obtained in collaboration with the TB
Modeling and Analysis Consortium (TB-MAC), and are shown in Table 5. The
disaggregation of incident TB into treated and not treated cases is based on the numbers
of notified cases adjusted for under-reporting.
Direct measurements of HIV-associated TB mortality are urgently needed. This is
especially the case for countries such as South Africa and Zimbabwe, where national VR
systems are already in place. In other countries, more efforts are required to initiate the
implementation of sample VR systems as an interim measure.
Disaggregation of TB mortality by age and sex
From the age-specific adjusted (for coverage and ill-defined causes) number of deaths
from VR, we first estimate the ratio r 2 of rates in children (M 0−14) compared to adults
(M 15+) (equation 7). The estimation of r 2 is based on a chained equations multiple
imputation approach. The imputation model covariates include total notifications,
population proportion aged more than 65 years, an indicator variable for the
epidemiological region and whether a country was one of the 30 HBCs37. The overall
mortality rate for all ages (M ) can be expressed as a weighted average of mortality in
children and adults, where c is the proportion of children among the general population
(equation 8)
(7)
(8)
In countries with VR or mortality survey data, M 0−14 is directly measured. For the sex
disaggregation of TB mortality among adults (M 15+), sex-specific adjusted (for coverage
and ill-defined causes) number of deaths from VR to estimate mortality rates in men M m
and women M w were used. The ratio of these rates r 3=M m /M w is either directly
measured in countries with VR data or imputed in countries without and sex-specific
mortality rates are then derived in a manner similar to shown in equation 6.
TB deaths among HIV-positive people were disaggregated by age and sex using the
assumption that the child to adult and men to women ratios are the same as the
corresponding ratios of AIDS deaths estimated by UNAIDS.
Estimating deaths averted
An estimate of the number of deaths averted was obtained by comparing a
counterfactual where all incident cases would be untreated, using CFRs for untreated TB
shown in Table 1, to a factual of TB mortality as estimated using methods described
above.
Estimation of uncertainty
There are many potential sources of uncertainty associated with estimates of TB
incidence, prevalence and mortality, as well as estimates of the burden of
HIV-associated TB and MDR-TB. These include uncertainties in input data, in
parameter values, in extrapolations used to impute missing data, and in the models
used. Uncertainty in population estimates was not accounted for.
Notification data are of uneven quality. Cases may be under-reported (for example,
missing quarterly reports from remote administrative areas are not uncommon),
misclassified (in particular, misclassification of recurrent cases in the category of new
cases is common), or over-reported as a result of duplicated entries in TB information
systems. The latter two issues can only be addressed efficiently in countries with
case-based nationwide TB databases that include patient identifiers. Sudden changes in
notifications over time are often the result of errors or inconsistencies in reporting.
Uncertainty bounds and ranges were defined as the 2.5th and 97.5th centiles of outcome
distributions. The general approach to uncertainty analyses was to draw values from
specified distributions in Monte Carlo simulations or else, uncertainty was propagated
analytically by approximating the mean and the second central moment of functions of
random variables using higher-order Taylor series expansion38 in a matrix based
approach39.
Conclusion
The measurement methods described here can be combined to assess tuberculosis
incidence and mortality, to evaluate progress towards targets for tuberculosis control
and the SDGs for TB. Alternative TB burden estimation methods have been developed
by the Institute of Health Metrics and Evaluation40, with generally consistent results at
the global level compared with WHO, but with marked differences in specific countries.
Discrepancies in estimates from different agencies reflect the questionable quality and
completeness of the underlying data. Further convergence in estimates will result from
improvements in measurements at country level. National control programmes should
be able to measure the level and time trends in incidence through well-performing TB
surveillance with universal access to health. In countries with incomplete routine
surveillance, prevalence surveys of TB disease provide estimates of TB burden that do
not heavily rely on expert opinion. The performance of TB surveillance should be
assessed periodically10 and the level of under-reporting should be measured9 and
minimized. Tuberculosis mortality will ideally be measured by counting deaths in a
comprehensive vital registration system35.
WHO’s post-2015 global TB strategy, known as the End TB Strategy41, has the goal of
ending the global TB epidemic, with corresponding targets of a 90% reduction in TB
deaths and an 80% reduction in the TB incidence rate by 2030, compared with 2015.
Improved measurements through substantial investments in health information
systems, TB surveillance and the broader SDG agenda will provide a firmer basis for
monitoring progress towards the End TB Strategy targets and ultimate TB elimination.
Acknowledgements Ibrahim Abubakar, Sandra Alba, Elisabeth Allen, Martien Borgdorff, Jaap Broekmans,
Ken Castro, Frank Cobelens, Ted Cohen, Charlotte Colvin, Sarah Cook-Scalise, Liz
Corbett, Simon Cousens, Pete Dodd, Katherine Fielding, Peter Godfrey-Faussett, Rein
Houben, Helen Jenkins, Li Liu, Mary Mahy, Valérie Schwoebel, Cherise Scott, James
Seddon, Andrew Thomson, Edine Tiemersma, Hazim Timimi, Theo Vos, Emilia
Vynnycky and Richard White reviewed the described methods to derive TB incidence,
prevalence and mortality with disaggregation by age and sex and provided specific
recommendations to improve them.
Annex 1 - Definitions Incidence is defined as the number of new and recurrent (relapse) episodes of TB (all
forms) occurring in a given year. Recurrent episodes are defined as a new episode of TB
in people who have had TB in the past and for whom there was bacteriological
confirmation of cure and/or documentation that treatment was completed.
Prevalence is defined as the number of TB cases (all forms) at a the middle of the year.
Mortality from TB is defined as the number of deaths caused by TB in HIV-negative
people occurring in a given year, according to the latest revision of the International
classification of diseases (ICD-10). TB deaths among HIV-positive people are classified
as HIV deaths in ICD-10. For this reason, estimates of deaths from TB in HIV-positive
people are presented separately from those in HIV-negative people.
The case fatality rate is the risk of death from TB among people with active TB
disease.
The case notification rate refers to new and recurrent episodes of TB notified for a
given year. Patients reported in the unknown history category are considered incident
TB episodes (new or recurrent).
Population estimates were the 2015 revision of the World Population Prospects,
which is produced by the United Nations Population Division (UNPD,
http://esa.un.org/unpd/wpp/). The UNPD estimates sometimes differ from those made
by countries.
Annex 2 - Relationship between HIV prevalence in new TB cases
and HIV prevalence in the general population
Let I and N denote incident cases and the total population, respectively, superscripts +
and - denote HIV status, ϑ is the prevalence of HIV among new TB cases, h is the
prevalence of HIV in the general population and ρ is the incidence rate ratio
(HIV-positive over HIV-negative).
The TB incidence rate ratio ρ can be estimated by fitting the following linear model with
a slope constrained to 1
Annex 3 - Implementation steps
The methods described in the paper were implemented in the following steps:
1. Reviewing available prevalence measurements from surveys, adjusting for
childhood TB and bacteriologically unconfirmed TB;
2. estimating overall TB incidence after review and cleaning of case notification
data;
3. cleaning and adjusting raw mortality data from VR systems and mortality
surveys, followed by imputation of missing values in countries with VR or survey
data;
4. cleaning of measurements of HIV prevalence among TB patients followed by
estimating HIV-positive TB incidence and HIV-positive TB mortality;
5. estimating HIV-negative TB mortality in countries with no VR data;
6. estimating incidence and mortality disaggregated by age and sex and
disaggregated by drug resistance status.
Tables
Table 1: Distribution of disease duration by case category
Case category Distribution of disease duration
(year)
Treated, HIV-negative Uniform (0.2−2)
Not treated, HIV-negative Uniform (1−4)
Treated, HIV-positive Uniform(0.01−1)
Not treated, HIV-positive Uniform (0.01−0.2)
Table 2: Incidence estimation based on U /T
U (n )
T (n )
Prevalence (10-3)
Duration (year)
Incidence (10-3y-1)
Cambodia 2002 260 42 12 (10-15) 2.9 (1.9-4) 4 (2.5-5.8)
Cambodia 2011 205 80 8.3 (7.1-9.8) 1.2 (0.8-1.6) 6.7 (4.5-9.3)
Myanmar 2009 300 79 6.1 (5-7.5) 1.8 (1.1-1.6) 3.3 (2-4.8)
Thailand 2012 136 60 2.5 (1.9-3.5) 1.1 (0.5-1.6) 2.3 (1-3.5)
Table 3: Estimates of incidence derived from prevalence survey results, based on two
estimation methods.
Prevalence
(10 -3)
Incidence - Method 1
(10 -3 y -1 )
Incidence - Method 2
(10 -3 y -1 )
Cambodia 2002 12 (10-15) 4 (2.5-5.8) 2.2 (1.5-2.9)
Cambodia 2011 8.3 (7.1-9.8) 6.7 (4.5-9.3) 3.8 (2.2-5.8)
Myanmar 2009 6.1 (5-7.5) 3.3 (2-4.8) 3.5 (2-5.1)
Thailand 2012 2.5 (1.9-3.5) 2.3 (1-3.5) 1.1 (0.7-1.6)
Table 4: Distribution of CFRs by case category
CFR Sources
Not on TB treatment f u 0.43 (0.28-0.53) 42,43
On TB treatment f t 0.03 (0-0.07) 44
Table 5: Distribution of CFR in HIV-positive individuals
ART TB
treatment
CFR Sources
off off 0.78 (0.65-0.94) 42
off on 0.09 (0.03-0.15) 44,45
< 1 year off 0.62 (0.39-0.86) Data from review + assumptions
< 1 year on 0.06 (0.01-0.13) Data from review + assumptions
≥ 1 year off 0.49 (0.31-0.70) Assumptions
≥ 1 year on 0.04 (0.00-0.10) Assumptions
Figures
Fig. 1 Main method to estimate TB incidence. In the first method, case notification data are combined with expert opinion about case detection gaps (under-reporting and under-diagnosis), and trends are estimated using either mortality data, repeat surveys of the annual risk of infection or exponential interpolation using estimates of case detection gaps for three years. For all high-income countries except the Netherlands and the United Kingdom, notifications are adjusted by a standard amount or measures of under-reporting from inventory studies, to account for case detection gaps.
Fig 2. Estimates of TB incidence obtained indirectly (in blue) based on case notifications and expert opinion about detection and reporting gaps prior to a recent national prevalence survey, and derived from prevalence using survey results (in red), corresponding to the survey year (2012-2015). The length of segments indicate uncertainty ranges. Countries are presented in the order of the difference between estimates, grouped by continent. Estimates derived from prevalence are more robust. Post-survey estimates resulted in significant increases in estimated incidence in Nigeria and Indonesia, two countries with large population sizes resulting in a notable impact on global estimates. The often narrow uncertainty ranges of pre-survey estimates reflect a certain level of overconfidence of experts asked to estimate plausibility ranges for incidence. The post-survey estimate in Tanzania is highly uncertain, reflecting data management problems at the time of the survey as well as incomplete laboratory data.
Fig. 3 HIV prevalence rate ratio among prevalent TB cases over notified TB cases in 5 countries (points with area proportional to inverse variance) against HIV prevalence in notified cases. Predicted values of the prevalence rate ratio are shown with a solid line. The dashed lines represent 95% confidence bounds.
Fig. 4 Global progress in reporting of TB cases among children, 1995–2015(a). Left panel: Number of notifications of cases among children reported to WHO (blue: 0-14, red: 5-14, green: 0-4). Right panel: Percentage of case notifications reported to WHO that are age-disaggregated (blue: new smear positive, green-dashed: new smear-negative and smear not done, red dash: new extra-pulmonary, red: new and relapse all forms)
(a) Before 2013 childhood case notifications included smear-positive, smear-negative, smear not done and extrapulmonary TB for all new patients. After 2013 (shown as a gap in the graph) childhood case notification include all new and relapse cases irrespective of case type.
Fig. 5 Countries (n =128, in green) for which TB mortality is estimated using measurements from
vital registration systems and/or mortality surveys
Fig. 6 Comparison of VR mortality (HIV-negative), horizontal axis (log scale) and mortality
predicted as the product of incidence and CFR, vertical axis (log scale). Horizontal and vertical
segments indicate uncertainty intervals. The dashed red line shows equality. The blue line and
associated grey banner show the least-squared best fit to the data, with a slope not constrained to
one.
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